RAG accuracy tables show reasoning can help or reduce scores

The customer problem is an assistant that reads prose adequately but struggles with tables or complex questions. Engage records owners, service managers and platform staff. Ask which errors require human correction, what document formats dominate, and whether external processing is permitted. Offer a bounded comparison on one approved corpus. The value hypothesis is better evidence for selecting features; do not promise savings or transfer benchmark scores to agency accuracy. Include correction time and user success in discovery, rather than assuming higher judged scores improve the workflow.

Freeze a representative corpus, label expected answers and compare reasoning settings under identical retrieval conditions. Treat captioning as a separate experimental factor. Capture stage latency, output support and failed requests alongside judged quality. Prerequisites include approved model access, sufficient GPU memory and a controlled evaluation dataset. Apply document authorization to extracted images and traces as well as text. Use a human-scored holdout to test whether automated ranking matches domain judgment. The proof of value should identify useful configurations for each task type, with explicit regressions.

Assign the knowledge-service owner responsibility for acceptance, supported by data engineers and domain reviewers. Build ingestion checks, a reproducible test manifest and an error-reporting workflow. Dependencies include document permissions, reviewer time and monitoring capacity. Train users to inspect cited evidence and retain an accessible manual route. Proposed acceptance is a reviewed result for every agreed task category, no unauthorized document retrieval in boundary tests, and response times within locally agreed limits. Review governance before expansion; risks include stale corpora and silent quality changes after updates.